CN107729767A - Community network data-privacy guard method based on figure primitive - Google Patents
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Abstract
The present invention discloses a kind of community network data-privacy guard method based on figure primitive, and it is converted to weighted graph after initialization data, by original no weight graph, and the number of degrees descending of node of graph is arranged and K degree anonymity algorithms are carried out to it, obtains the anonymous degree series of descending；It is poor that anonymous degree series and original degree series are made, and to carry out the node of side modification according to difference classification and be combined into Candidate Set；According to the modification of corresponding selection standard until meeting anonymous require；Community network data after issue is anonymous.The present invention is in delivery network data, and while ensureing that anonymity requires, the figure primitive structure that has been effectively maintained in network is advantageous to analysis of the data analysis person to related data.
Description
Technical field
The present invention relates to data-privacy protection technique field, and in particular to a kind of community network data based on figure primitive are hidden
Private guard method.
Background technology
In recent years, community network becomes to become more and more popular, so that increasing people begins to use online community network
Go to exchange with friend, household, colleague.Go by community network to share some personal information just because of many people, so society
Meeting network turns into the significant data source that many fields are studied and excavated.The private letter of many users is embedded in data
Breath, so data owner should avoid some private sensitive information leakages of user when data publication is come out, because
This, anonymity processing becomes particularly important.
At present, people are only realized to social network analysis by capturing low order structure (node and side) in network.
However, also comprising many small network subgraphs (being referred to as motif or figure primitive) in some networks, they are referred to as high-order knot
Structure, these figure primitives include many information, and a new direction is provided for data miner.Figure primitive has many applications, such as
In research transportation network, these figure primitives will be regarded as a base unit, for considering traffic accessibility；In terms of medical science
Genetic transcription, using in this figure Element recognition network difference in functionality module (drug resistance etc.) come preferably study medicine；
In the Food web of living nature, energy level stream is divided.The figure primitive being made up of 3 nodes, as triangle, it is in social network
A kind of relation may be represented in network, as " friends of friends are friends " or " enemy of enemy is friend " etc..Due in society
Hand over figure primitive in network to be considered as a kind of closely coupled relation, therefore while anonymous processing is carried out, should protect as far as possible
Protect this figure primitive structure.
Traditional simple anonymous secret protection technology has been not enough to protect the privacy of user, in order to preferably protect society
The privacy of data in network, now popular anonymity technology have：Node K- is anonymous, i.e., each node at least with K-1 its
His node undistinguishable, then the probability that each node is successfully identified is no more than 1/K；K degree is anonymous, that is, assumes that attacker knows
Degree (side) information of all nodes in road, anonymous at least K-1 node undistinguishable afterwards.Also other anonymous methods, example
Such as randomization, difference privacy.But in the secret protection research of today's society network, more focus on privacy concern, and
The availability of issue data is have ignored, because in actual life, the excavation for community network data publication concerns with analysis
The very multidirectional development of social science, such as biology, medical science etc..It is necessary so improving existing anonymous methods.
The content of the invention
To be solved by this invention is that existing method for secret protection only considers catch net when anonymity handles community network
The problem of institutional framework of some low orders in network, there is provided a kind of community network data-privacy guard method based on figure primitive,
It is when issuing community network data, while ensureing that anonymity requires, preferably protects the high-order structures (figure primitive) in network,
Be advantageous to data analysis person preferably to utilize.
To solve the above problems, the present invention is achieved by the following technical solutions：
Community network data-privacy guard method based on figure primitive, it is as follows to specifically include step：
Step 1, the number for the figure primitive for being participated in each edge of original graph change original graph as the weight on the side
Into weighted graph；
Step 2, the original degree series application K- degree anonymity algorithms to weighted graph, it is met the anonymous anonymous degree series of K；
Step 3, the degree that the degree of anonymous degree series interior joint is subtracted to corresponding node in original degree series, and according to each section
The difference of point degree is classified to node, and node of the difference more than zero is placed in increase degree node set VS+, difference is less than
Zero node is placed in reduction rate node set VS-, and the null node of difference is not processed；
Step 4, the number of degrees summation for judging all nodes in anonymous sequence and the number of degrees summation of all nodes in original series
It is whether equal；
If the number of degrees summation of anonymous sequence is more than the number of degrees summation of original series, goes to step 5 and carry out side insertion behaviour
Make；
If the number of degrees summation of anonymous sequence is less than the number of degrees summation of original series, goes to step 6 and carry out edge contract behaviour
Make；
If the number of degrees summation of anonymous sequence is equal to the number of degrees summation of original series, goes to step 7 and carry out side conversion behaviour
Make；
Step 5, side insertion operation：Combination of two is first carried out respectively to all nodes in increase degree node set VS+, and sentenced
It whether there is side between this disconnected 2 nodes；If there is side, then do not process；If there is no side, then by this 2 nodes it
Between side by add insert line set in；Afterwards using the insertion line set as Candidate Set, step 8 is gone to；
Step 6, edge contract operation：Combination of two is first carried out respectively to all nodes in reduction rate node set VS-, and sentenced
It whether there is side between this disconnected 2 nodes；If there is no side, then do not process；If there is side, then by this 2 nodes it
Between side add delete line set in；Afterwards using the deletion line set as Candidate Set, step 8 is gone to；
Step 7, side conversion operation：It is first each node 1 best neighbor section of selection in reduction rate node set VS-
Point, wherein best neighbor node are the minimum node of the weight on side between the node in all neighbor nodes of the node；Again
Best neighbor node is combined with each node in increase degree node set VS+ respectively, and judged between this 2 nodes
With the presence or absence of side；If there is side, then do not process；If there is no side, then the side between this 2 nodes will be added and changed
In line set；Afterwards using the conversion line set as Candidate Set, step 8 is gone to；
Step 8, select one group of side operation in Candidate Set correspondingly to change original graph one by one, and judge amended
Whether original graph meets anonymous requirement；If meeting anonymous requirement, terminate；If being unsatisfactory for anonymous requirement, candidate is selected
Next group of side operation is concentrated to modify original graph, until meeting anonymous require；If perform all of complete individual Candidate Set
After the operation of side, amended original graph is still unsatisfactory for anonymous requirement, then original graph is updated into amended original graph, and turn
To step 3.
In such scheme, figure primitive is shaped as polygon.
In above-mentioned steps 2, before the original degree series application K- degree anonymity algorithms to weighted graph, it is also necessary to weighted graph
Original degree series according to the number of degrees carry out descending sort.
In above-mentioned steps 6, Candidate Set is after carrying out ascending sort to weight while according to this deleted in line set
Formed.
In above-mentioned steps 8, using the Candidate Set formed based on deletion line set, original graph is carried out to delete side modification
When, the modification to original graph is terminated on the modification since Candidate Set the minimum side of weight to original graph, the side maximum from weight.
In above-mentioned steps 5, Candidate Set is to carry out descending to neighbours' centrality while according to this in insertion line set
Formed after sequence.
In above-mentioned steps 7, Candidate Set is to carry out descending to neighbours' centrality while according to this in conversion line set
Formed after sequence.
In above-mentioned steps 8, using the Candidate Set formed based on insertion line set and conversion line set, original graph is carried out
When insertion is changed while with conversion, the modification since Candidate Set the maximum side of neighbours' centrality to original graph, from neighbours
Terminate the modification to original graph in the minimum side of disposition.
In such scheme, 2 node viAnd vjBetween side neighbours centrality NC (vi,vj) calculation formula be：
In formula, N (vi) it is node viNeighbor node set, N (vj) it is node vjNeighbor node set, | | represent
Seek the number of set interior joint.
In such scheme, in step 8, amended original graph meet it is anonymous require to refer to, amended original graph it is every
The degree of individual node is consistent with the degree of corresponding node in anonymous sequence.
Compared with prior art, the present invention is carrying out the process of figure modification for protection high-order institutional framework (figure primitive)
In, delete while when pay the utmost attention to while weight (size of weight represents that side participates in the number of figure primitive) perform deletion every time
When, side right weight minimum is selected, the figure primitive number so destroyed is few；Increase while when consider while neighbours' centrality, neighbours center
Property the bigger explanation node of numerical value between compactness it is stronger, when performing increase side every time, consider that selection neighbours' centrality value is big,
To ensure imporosity.
Brief description of the drawings
Fig. 1 is the flow chart of the community network data-privacy guard method of the invention based on figure primitive.
Fig. 2 is the flow chart of preferred embodiment of the present invention.
Fig. 3 is the community network original graph of preferred embodiment of the present invention.
Fig. 4 is the community network weighted graph of preferred embodiment of the present invention.
Fig. 5 is the community network issue figure after preferred embodiment anonymity of the present invention.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with instantiation, and with reference to attached
Figure, the present invention is described in more detail.
A kind of community network data-privacy guard method based on figure primitive, as shown in figure 1, it is as follows to specifically include step：
Step 1, the number for the figure primitive for being participated in each edge of original graph change original graph as the weight on the side
Into weighted graph；
Step 2, the original degree series application K- degree anonymity algorithms to weighted graph, it is met the anonymous anonymous degree series of K；
Step 3, the degree that the degree of anonymous degree series interior joint is subtracted to corresponding node in original degree series, and according to each section
The difference of point degree is classified to node, and node of the difference more than zero is placed in increase degree node set VS+, difference is less than
Zero node is placed in reduction rate node set VS-, and the null node of difference is not processed；
Step 4, the number of degrees summation for judging all nodes in anonymous sequence and the number of degrees summation of all nodes in original series
It is whether equal；
If the number of degrees summation of anonymous sequence is more than the number of degrees summation of original series, goes to step 5 and carry out side insertion behaviour
Make；
If the number of degrees summation of anonymous sequence is less than the number of degrees summation of original series, goes to step 6 and carry out edge contract behaviour
Make；
If the number of degrees summation of anonymous sequence is equal to the number of degrees summation of original series, goes to step 7 and carry out side conversion behaviour
Make；
Step 5, side insertion operation：Combination of two is first carried out respectively to all nodes in increase degree node set VS+, and sentenced
It whether there is side between this disconnected 2 nodes；If there is side, then do not process；If there is no side, then by this 2 nodes it
Between side by add insert line set in；Afterwards using the insertion line set as Candidate Set, step 8 is gone to；
Step 6, edge contract operation：Combination of two is first carried out respectively to all nodes in reduction rate node set VS-, and sentenced
It whether there is side between this disconnected 2 nodes；If there is no side, then do not process；If there is side, then by this 2 nodes it
Between side add delete line set in；Afterwards using the deletion line set as Candidate Set, step 8 is gone to；
Step 7, side conversion operation：It is first each node 1 best neighbor section of selection in reduction rate node set VS-
Point, wherein best neighbor node are the minimum node of the weight on side between the node in all neighbor nodes of the node；Again
Best neighbor node is combined with each node in increase degree node set VS+ respectively, and judged between this 2 nodes
With the presence or absence of side；If there is side, then do not process；If there is no side, then the side between this 2 nodes will be added and changed
In line set；Afterwards using the conversion line set as Candidate Set, step 8 is gone to；
Step 8, select one group of side operation in Candidate Set correspondingly to change original graph one by one, and judge amended
Whether original graph meets anonymous requirement；If meeting anonymous requirement, terminate；If being unsatisfactory for anonymous requirement, candidate is selected
Next group of side operation is concentrated to modify original graph, until meeting anonymous require；If perform all of complete individual Candidate Set
After the operation of side, amended original graph is still unsatisfactory for anonymous requirement, then original graph is updated into amended original graph, and turn
To step 3.
In the present invention, figure primitive is shaped as polygon, such as can be triangle, quadrangle, some are small for pentagon
Subgraph structure.Below exemplified by triangular graph primitive, by an instantiation to a kind of society based on figure primitive of the present invention
Network data method for secret protection is further elaborated, and referring to 2, it specifically comprises the following steps：
Step 1, initialization data, the community network original graph shown in Fig. 3 is converted to the community network shown in Fig. 4 and weighted
Figure.
Step 2, the node set V of community network weighted graph is subjected to descending arrangement, original degree series by the number of degrees first
{‘2’：6, ' 1 '：5, ' 6 '：4, ' 4 '：3, ' 8 '：3, ' 9 '：3, ' 3 '：2, ' 5 '：2, ' 7 '：2, ' 10 '：2, ' 11 '：2 } in order to rear
Continuous processing.Then K- degree anonymity algorithms are run, obtain anonymous degree series { ' 2 '：5, ' 1 '：5, ' 6 '：4, ' 4 '：4, ' 8 '：3,
‘9’：3, ' 3 '：2, ' 5 '：2, ' 7 '：2, ' 10 '：2, ' 11 '：2}.
Step 3, anonymous degree series and original degree series are made first it is poor, will the degree of anonymous degree series interior joint subtract original
The degree of corresponding node in beginning degree series, obtains difference set { ' 2 '：- 1, ' 1 '：0, ' 6 '：0, ' 4 '：1, ' 8 '：0, ' 9 '：0, ' 3 '：0,
‘5’：0, ' 7 '：0, ' 10 '：0, ' 11 '：0}.Then difference set is classified according to difference：(1) corresponding node more than zero will be worth
It is put into increase degree node set VS+={ ' 4 ' }；(2) minus corresponding node will be worth and be put into reduction rate node set VS-=
{‘2’}；(3) the null node of difference is not processed.
Step 4, the number of degrees summation of more original degree series and the number of degrees summation of anonymous degree series：If the degree of anonymous sequence
Number summation is more than the number of degrees summation of original series, then carries out the side insertion operation of step 5；If the number of degrees summation of anonymous sequence is small
In the number of degrees summation of original series, then the edge contract operation of step 6 is carried out；If the number of degrees summation of anonymous sequence is equal to original sequence
The number of degrees summation of row, then carry out the side conversion operation of step 7.
In the present embodiment, the original number of degrees and for 34, the anonymous number of degrees and be 34 because both are equal, select side to turn
Change operation.
Step 5, side insertion operation ins={ VS+ }：
First, all nodes carry out combination of two (increase degree section in the present embodiment respectively in increase degree node set VS+
Point set VS+={ ' 4 ' }, due to there was only 1 node, therefore without combination.Assuming that more than one node in increase degree set of node VS+,
Such as the so so-called combination of two of VS+={ ' 1 ', ' 2 ', ' 3 ' } is exactly (' 1 ', ' 2 '), (' 1 ', ' 3 '), (' 2 ', ' 3 ') this 3
Kind combination), and judge to whether there is side between this 2 nodes；If there is side, then do not process；If there is no side, then will
Side between this 2 nodes is added in insertion line set.
Then, candidate is formed after carrying out descending sort to neighbours' centrality while according to this in insertion line set
Collection, and go to step 8.
2 node viAnd vjBetween side neighbours centrality NC (vi,vj) calculation formula be：
In formula, N (vi) it is node viNeighbor node set, N (vj) it is node vjNeighbor node set, | | represent
Seek the number of set interior joint.
Step 6, edge contract operation del={ VS- }：
First, all nodes in reduction rate node set VS- are carried out with combination of two respectively (in the present embodiment, to reduce
Node set VS-={ ' 2 ' } is spent, due to there was only 1 node, therefore without combination.Assuming that more than one in increase degree set of node VS+
Node, such as the so so-called combination of two of VS+={ ' 4 ', ' 5 ', ' 6 ', ' 7 ' } is exactly (' 4 ', ' 5 '), (' 4 ', ' 6 '),
(' 4 ', ' 7 '), (' 5 ', ' 6 '), (' 5 ', ' 7 '), (' 6 ', ' 7 ') this 6 kinds combinations), and judge to whether there is between this 2 nodes
Side；If there is no side, then do not process；If there is side, then the side between this 2 nodes is added and deleted in line set；
Then, Candidate Set is formed after ascending sort being carried out to weight while according to this deleted in line set, and is turned
To step 8.
Step 7, side conversion operation shift={ VS-, VS+ }：
First, 1 best neighbor node, wherein best neighbor are selected for each node in reduction rate node set VS-
Node is the minimum node of the weight on side between the node in all neighbor nodes of the node；
Then, best neighbor node is subjected to combination of two with each node in increase degree node set VS+ respectively, and
Judge to whether there is side between this 2 nodes；If there is side, then do not process；If there is no side, then by this 2 nodes
Between side by add change line set in；
Finally, candidate is formed after carrying out descending sort to neighbours' centrality while according to this in conversion line set
Collection, and go to step 8.
2 node viAnd vjBetween side neighbours centrality NC (vi,vj) calculation formula be：
In formula, N (vi) it is node viNeighbor node set, N (vj) it is node vjNeighbor node set, | | represent
Seek the number of set interior joint.
In the present embodiment, the neighbor node collection of reduction rate node set VS- interior joints ' 2 ' for ' 1 ', ' 3 ', ' 4 ',
' 5 ', ' 6 ', ' 7 ' }, if having in neighbor node it is identical with the node in increase degree node set VS+, by the node in neighbours
Deleted in set of node because the present embodiment interior joint ' 4 ' at the same appear in two set in, in neighbor node delete section
Point ' 4 ' is { ' 1 ', ' 3 ', ' 5 ', ' 6 ', ' 7 ' }, now shift=(' 4 ', ' 3 '), (' 4 ', ' 5 '), (' 4 ', ' 6 '), (' 4 ',
' 7 ') }, and according to neighbours' centrality when neighbours' centrality calculation formula calculates and descending arrange, then shift=
{ (' 4 ', ' 5 '), (' 4 ', ' 3 '), (' 4 ', ' 7 '), (' 4 ', ' 6 ') }.
Step 8, according to Candidate Set original graph is modified.
First, one group of side in Candidate Set is selected correspondingly to change original graph one by one.
The Candidate Set formed based on insertion line set, the insertion operation on side is carried out to original graph；Based on deletion line set
The Candidate Set formed, the deletion action on side is carried out to original graph；The Candidate Set that line set formed is turned based on side, to original graph
Carry out the conversion operation on side.
In modification, based on insertion line set and the Candidate Set that is formed of line set is changed, maximum from neighbours' centrality
While starting the modification to original graph, the modification to original graph is terminated on the side minimum from neighbours' centrality；Based on deletion line set institute
The Candidate Set of formation, the modification since the minimum side of weight to original graph, the side maximum from weight terminate to repair original graph
Change.
Then, judge whether amended original graph meets anonymous requirement；If meeting anonymous requirement, terminate；If
Anonymous requirement is unsatisfactory for, then selects next group of side operation in Candidate Set to modify original graph, until meeting anonymous require；Such as
After fruit performs all sides operation of complete Candidate Set, amended original graph is still unsatisfactory for anonymous requirement, then by original graph
Amended original graph is updated to, and goes to step 3.
In the present embodiment, because shift is descending arrangement, first, and neighbours' centrality are selected every time
Side addition maximum NC, if operating the anonymous requirement of complete fulfillment, terminates.Selected if being unsatisfactory for next in Candidate Set
Group side operation to original graph change, until currently modification figure degree series be equal to anonymous degree series, end.Else if perform
Whole group Candidate Set, still it is unsatisfactory for requiring, then by amended original graph, goes to step 3 and handle.According to front step in example
Operation, this example is that first side (' 2 ', ' 5 ' are deleted in selection), then add side (' 4', ' 5 '), the degree series for calculating figure after modification are
{‘2’：5, ' 1 '：5, ' 6 '：4, ' 4 '：4, ' 8 '：3, ' 9 '：3, ' 3 '：2, ' 5 '：2, ' 7 '：2, ' 10 '：2, ' 11 '：2 }, with anonymity
Degree series are compared, just equal, then algorithm terminates, and return to anonymous figure as shown in Figure 5.
It is below the preservation situation and convergence factor of figure primitive (triangle) of the algorithm of the invention designed in examples detailed above：
| Before anonymity | After anonymity | |
| Figure primitive (triangle) number | 7 | 7 |
| Average aggregate coefficient (CC) | 0.727272727273 | 0.715151515152 |
The present invention's is undirected, acyclic figures that community network data are not tape label, and the background knowledge of attacker is known
The degree information of all nodes.Need to carry out simple anonymous processing before community network data publication, exactly remove node only
One identifier, such as name, use number-mark instead.The figure of issue is represented with G (V, E) after anonymous, and V represents node, is mapped as
Individual in reality, E represent side, represent the relation between individual, such as friend, household etc..Figure is issued in the algorithm by the present invention
After processing, attacker can be effectively prevented uniquely to go to identify an individual using background knowledge, meanwhile, to the high-order of artwork
Institutional framework (figure primitive) there has also been corresponding protection, not only reach the purpose of protection individual so, also be data analysis person point
The relevant information of analysis high-order institutional framework provides availability.
It should be noted that although embodiment of the present invention is illustrative above, but it is to the present invention that this, which is not,
Limitation, therefore the invention is not limited in above-mentioned embodiment.Without departing from the principles of the present invention, it is every
The other embodiment that those skilled in the art obtain under the enlightenment of the present invention, it is accordingly to be regarded as within the protection of the present invention.
Claims (10)
1. the community network data-privacy guard method based on figure primitive, it is characterized in that, it is as follows to specifically include step：
Original graph is converted into adding by step 1, the number for the figure primitive for being participated in each edge of original graph as the weight on the side
Weight graph；
Step 2, the original degree series application K- degree anonymity algorithms to weighted graph, it is met the anonymous anonymous degree series of K；
Step 3, the degree that the degree of anonymous degree series interior joint is subtracted to corresponding node in original degree series, and according to each node degree
Difference node is classified, difference is placed in increase degree node set VS+ more than zero node, difference is minus
Node is placed in reduction rate node set VS-, and the null node of difference is not processed；
Whether the number of degrees summation of all nodes in step 4, the number of degrees summation for judging all nodes in anonymous sequence and original series
It is equal；
If the number of degrees summation of anonymous sequence is more than the number of degrees summation of original series, goes to step 5 and carry out side insertion operation；
If the number of degrees summation of anonymous sequence is less than the number of degrees summation of original series, goes to step 6 and carry out edge contract operation；
If the number of degrees summation of anonymous sequence is equal to the number of degrees summation of original series, goes to step 7 and carry out side conversion operation；
Step 5, side insertion operation：Combination of two is first carried out respectively to all nodes in increase degree node set VS+, and judges this
It whether there is side between 2 nodes；If there is side, then do not process；If there is no side, then by between this 2 nodes
While inserted adding in line set；Afterwards using the insertion line set as Candidate Set, step 8 is gone to；
Step 6, edge contract operation：Combination of two is first carried out respectively to all nodes in reduction rate node set VS-, and judges this
It whether there is side between 2 nodes；If there is no side, then do not process；If there is side, then by between this 2 nodes
Side, which adds, deletes in line set；Afterwards using the deletion line set as Candidate Set, step 8 is gone to；
Step 7, side conversion operation：It is first each node 1 best neighbor node of selection in reduction rate node set VS-, its
Middle best neighbor node is the minimum node of the weight on side between the node in all neighbor nodes of the node；Again will most
Good neighbor node is combined with each node in increase degree node set VS+ respectively, and judge between this 2 nodes whether
Side be present；If there is side, then do not process；If there is no side, then by between this 2 nodes while will add change while collection
In conjunction；Afterwards using the conversion line set as Candidate Set, step 8 is gone to；
Step 8, select one group of side operation in Candidate Set correspondingly to change original graph one by one, and judge amended original
Whether figure meets anonymous requirement；If meeting anonymous requirement, terminate；If being unsatisfactory for anonymous requirement, select in Candidate Set
Next group of side operation is modified to original graph, until meeting anonymous require；If perform all sides behaviour of complete Candidate Set
After work, amended original graph is still unsatisfactory for anonymous requirement, then original graph is updated into amended original graph, and goes to step
Rapid 3.
2. the community network data-privacy guard method based on figure primitive according to claim 1, it is characterised in that：Figure primitive
Be shaped as polygon.
3. the community network data-privacy guard method based on figure primitive according to claim 1, it is characterised in that：Step 2
In, before the original degree series application K- degree anonymity algorithms to weighted graph, it is also necessary to the original degree series of weighted graph according to
The number of degrees carry out descending sort.
4. the community network data-privacy guard method based on figure primitive according to claim 1, it is characterised in that：Step 6
In, Candidate Set is formed after carrying out ascending sort to weight while according to this deleted in line set.
5. the community network data-privacy guard method based on figure primitive according to claim 4, it is characterised in that：Step 8
In, using based on deleting the Candidate Set that is formed of line set, when delete side modification to original graph, weight is most from Candidate Set
Small side starts the modification to original graph, and the modification to original graph is terminated on the side maximum from weight.
6. the community network data-privacy guard method based on figure primitive according to claim 1, it is characterised in that：Step 5
In, Candidate Set is formed after carrying out descending sort to neighbours' centrality while according to this in insertion line set.
7. the community network data-privacy guard method based on figure primitive according to claim 1, it is characterised in that：Step 7
In, Candidate Set is formed after carrying out descending sort to neighbours' centrality while according to this in conversion line set.
8. the community network data-privacy guard method based on figure primitive according to claim 6 or 7, it is characterised in that：Step
In rapid 8, using the Candidate Set formed based on insertion line set and conversion line set, insertion is carried out to original graph while with conversion
During modification, the modification since Candidate Set the maximum side of neighbours' centrality to original graph, the side knot minimum from neighbours' centrality
Modification of the beam to original graph.
9. the community network data-privacy guard method based on figure primitive according to claim 6 or 7, it is characterised in that：2
Node viAnd vjBetween side neighbours centrality NC (vi,vj) calculation formula be：
<mrow>
<mi>N</mi>
<mi>C</mi>
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<msub>
<mi>v</mi>
<mi>i</mi>
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<mo>,</mo>
<msub>
<mi>v</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mrow>
<mo>|</mo>
<mi>N</mi>
<mrow>
<mo>(</mo>
<msub>
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</msub>
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</mrow>
<mo>&cap;</mo>
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<mrow>
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<msub>
<mi>v</mi>
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</msub>
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</mrow>
<mo>|</mo>
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<mn>2</mn>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mo>|</mo>
<mi>N</mi>
<mo>(</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
<mo>|</mo>
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<mo>|</mo>
<mi>N</mi>
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In formula, N (vi) it is node viNeighbor node set, N (vj) it is node vjNeighbor node set, | | expression ask set
The number of interior joint.
10. the community network data-privacy guard method based on figure primitive according to claim 1, it is characterised in that：Step 8
In, amended original graph meet it is anonymous require to refer to, the degree of each node of amended original graph with anonymous sequence
The degree of corresponding node is consistent.
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